Response to anticancer treatment is dictated not only by availability of molecular targets within the tumor, but also by the highly heterogenous, patient-specific composition of cells within tumors and their complex tumor microenvironment (TME). Consequently, some patients demonstrate remarkable responses to anticancer therapies, whereas others experience negligible effects. Thus, there is an urgent need for more effective strategies to preselect patients for therapies, mitigating the risk of administering ineffective and potentially harmful drugs and speeding up the process for patients to receive the most effective treatment.

The most promising solution to tackle heterogenous responses to treatment is the creation of patient-derived models (‘patient avatars’) that accurately represent an individual’s tumor. Tailoring therapies for cancer patients based on their avatars’ responses to anticancer drugs could change the entire approach to treating patients with solid tumor diseases. Although introducing these human in vitro systems into clinical settings has been challenging, recent evidence has demonstrated the readiness of these models for clinical translation.

对抗癌治疗的反应不仅取决于肿瘤内分子靶标的可用性，还取决于肿瘤内高度异质的、患者特异性的细胞组成及其复杂的肿瘤微环境 (TME)。因此，一些患者对抗癌治疗表现出显着的反应，而另一些患者的效果可以忽略不计。因此，迫切需要更有效的策略来预先选择患者进行治疗，降低施用无效和潜在有害药物的风险，并加快患者接受最有效治疗的过程。

解决治疗异质反应的最有希望的解决方案是创建准确代表个体肿瘤的患者衍生模型（“患者化身”）。根据癌症患者的化身对抗癌药物的反应来定制治疗方法可能会改变治疗实体瘤疾病患者的整个方法。尽管将这些人体体外系统引入临床环境具有挑战性，但最近的证据表明这些模型已做好临床转化的准备。